James Faghmous, Mount Sinai
ICML 2016 WorkshopsJune 24, New York City, @ICML
#Data4Good: Machine Learning in Social Good Applications
The goal of this workshop is to bring together experts from different fields of machine learning, statistics, data science, social sciences and social activism to explore the opportunities for machine learning in applications with social impact. The workshop will consist of: 1) invited presentations from the leading practitioners in the field, and 2) a series of 20 minute presentations on research that fits the theme of machine learning for social good; broadly construed, this could be machine learning related social good applications, or new machine learning methods or theory of particular interest for social good applications.
We are experiencing a time when our lives and everything that surrounds us is captured digitally: Internet activity, video, customer transactions, surveys, health records, news, literature, scientific publications, economic data, weather data, geospatial data, stock market returns, telecommunication records, and government records to name a few. All of this data is at our fingertips, giving us an unprecedented opportunity to change the world for the better using machine learning and algorithms grounded in data. From reducing or eliminating inequalities, to improving access to health care and education, to reducing pollution and our carbon footprint, there is the potential to move the needle on seemingly insurmountable issues.
Technologies that incorporate machine learning toolkit to address humanitarian problems are gaining momentum. Yet, such applications often pose challenges to the state of the art machine learning algorithms. From oftentimes small, sparse and unreliable data, to learning from complementary multiple sources, to privacy, fairness and diversity, to interpretable and causal models in support of better decision making, to the applications of deep learning for satellite image analysis, to speech tagging and natural language processing, to algorithm evaluation and impact measurement in order to quantify the returns on social good initiatives and programs. Furthermore, social good applications are highly interdisciplinary in nature and require close collaboration between machine learning practitioners, subject matter experts and social sector experts.
Our goal is to raise awareness among machine learning practitioners about the opportunities in Data-for-Good movement, show them how valuable their skills can be and push the boundaries on addressing tough humanitarian challenges.
Rayid Ghani, Univeristy of Chicago
Matt Ghee, University of Chicago
Gideon Mann, Bloomberg
Aleksandra Mojsilovic, IBM Research
Kush Varshney, IBM Research